IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i13p4014-d588012.html
   My bibliography  Save this article

A New Chaotic Artificial Bee Colony for the Risk-Constrained Economic Emission Dispatch Problem Incorporating Wind Power

Author

Listed:
  • Motaeb Eid Alshammari

    (Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Makbul A. M. Ramli

    (Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Ibrahim M. Mehedi

    (Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

Due to the rapid increase in the consumption of electrical energy and the instability of fossil fuel prices, renewable energy, such as wind power (WP), has become increasingly economically competitive compared to other conventional energy production methods. However, the intermittent nature of wind energy creates certain challenges to the power network operation. The combined economic environmental dispatch (CEED) including WP is one of the most fundamental challenges in power system operation. Within this context, this paper presents a new attempt to solve the probabilistic CEED problem with WP penetration. The optimal WP to be incorporated in the grid is determined in such a way that the system security is within acceptable limits. The system security is described by various fuzzy membership functions in terms of the probability that power balance cannot be met. These membership functions are formulated based on the dispatcher’s attitude. This probabilistic and non-convex CEED problem is solved using a new technique combining chaos theory and artificial bee colony (ABC) technique. In this improved version of ABC (IABC), chaotic maps are used to generate initial solutions, and the random numbers involved in the standard ABC are substituted by chaotic sequences. The effectiveness of IABC is tested on two groups of benchmark functions and practical cases. The impacts of dispatcher’s attitude and risk level are investigated in the simulation section.

Suggested Citation

  • Motaeb Eid Alshammari & Makbul A. M. Ramli & Ibrahim M. Mehedi, 2021. "A New Chaotic Artificial Bee Colony for the Risk-Constrained Economic Emission Dispatch Problem Incorporating Wind Power," Energies, MDPI, vol. 14(13), pages 1-24, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:4014-:d:588012
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/13/4014/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/13/4014/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tutueva, Aleksandra V. & Nepomuceno, Erivelton G. & Karimov, Artur I. & Andreev, Valery S. & Butusov, Denis N., 2020. "Adaptive chaotic maps and their application to pseudo-random numbers generation," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    2. Alham, M.H. & Elshahed, M. & Ibrahim, Doaa Khalil & Abo El Zahab, Essam El Din, 2016. "A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management," Renewable Energy, Elsevier, vol. 96(PA), pages 800-811.
    3. Li, Y.Z. & Li, K.C. & Wang, P. & Liu, Y. & Lin, X.N. & Gooi, H.B. & Li, G.F. & Cai, D.L. & Luo, Y., 2017. "Risk constrained economic dispatch with integration of wind power by multi-objective optimization approach," Energy, Elsevier, vol. 126(C), pages 810-820.
    4. A. Cano-Ortega & F. Sánchez-Sutil, 2020. "Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings," Energies, MDPI, vol. 13(3), pages 1-29, January.
    5. Jin, Jingliang & Zhou, Dequn & Zhou, Peng & Miao, Zhuang, 2014. "Environmental/economic power dispatch with wind power," Renewable Energy, Elsevier, vol. 71(C), pages 234-242.
    6. Motaeb Eid Alshammari & Makbul A. M. Ramli & Ibrahim M. Mehedi, 2020. "An Elitist Multi-Objective Particle Swarm Optimization Algorithm for Sustainable Dynamic Economic Emission Dispatch Integrating Wind Farms," Sustainability, MDPI, vol. 12(18), pages 1-15, September.
    7. Ouannas, Adel & Khennaoui, Amina-Aicha & Odibat, Zaid & Pham, Viet-Thanh & Grassi, Giuseppe, 2019. "On the dynamics, control and synchronization of fractional-order Ikeda map," Chaos, Solitons & Fractals, Elsevier, vol. 123(C), pages 108-115.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Motaeb Eid Alshammari & Makbul A. M. Ramli & Ibrahim M. Mehedi, 2022. "Hybrid Chaotic Maps-Based Artificial Bee Colony for Solving Wind Energy-Integrated Power Dispatch Problem," Energies, MDPI, vol. 15(13), pages 1-26, June.
    2. Benyekhlef Larouci & Ahmed Nour El Islam Ayad & Hisham Alharbi & Turki E. A. Alharbi & Houari Boudjella & Abdelkader Si Tayeb & Sherif S. M. Ghoneim & Saad A. Mohamed Abdelwahab, 2022. "Investigation on New Metaheuristic Algorithms for Solving Dynamic Combined Economic Environmental Dispatch Problems," Sustainability, MDPI, vol. 14(9), pages 1-27, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jin, Jingliang & Zhou, Peng & Li, Chenyu & Bai, Yang & Wen, Qinglan, 2020. "Optimization of power dispatching strategies integrating management attitudes with low carbon factors," Renewable Energy, Elsevier, vol. 155(C), pages 555-568.
    2. Jin, Jingliang & Zhou, Peng & Li, Chenyu & Guo, Xiaojun & Zhang, Mingming, 2019. "Low-carbon power dispatch with wind power based on carbon trading mechanism," Energy, Elsevier, vol. 170(C), pages 250-260.
    3. Motaeb Eid Alshammari & Makbul A. M. Ramli & Ibrahim M. Mehedi, 2022. "Hybrid Chaotic Maps-Based Artificial Bee Colony for Solving Wind Energy-Integrated Power Dispatch Problem," Energies, MDPI, vol. 15(13), pages 1-26, June.
    4. Souad Bensid Ahmed & Adel Ouannas & Mohammed Al Horani & Giuseppe Grassi, 2022. "The Discrete Fractional Variable-Order Tinkerbell Map: Chaos, 0–1 Test, and Entropy," Mathematics, MDPI, vol. 10(17), pages 1-13, September.
    5. Rahmani, Shima & Amjady, Nima, 2017. "A new optimal power flow approach for wind energy integrated power systems," Energy, Elsevier, vol. 134(C), pages 349-359.
    6. Loau Al-Bahrani & Mehdi Seyedmahmoudian & Ben Horan & Alex Stojcevski, 2021. "Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations," Sustainability, MDPI, vol. 13(3), pages 1-26, January.
    7. Jin, Jingliang & Wen, Qinglan & Cheng, Siqi & Qiu, Yaru & Zhang, Xianyue & Guo, Xiaojun, 2022. "Optimization of carbon emission reduction paths in the low-carbon power dispatching process," Renewable Energy, Elsevier, vol. 188(C), pages 425-436.
    8. Sheng, Wanxing & Li, Rui & Yan, Tao & Tseng, Ming-Lang & Lou, Jiale & Li, Lingling, 2023. "A hybrid dynamic economics emissions dispatch model: Distributed renewable power systems based on improved COOT optimization algorithm," Renewable Energy, Elsevier, vol. 204(C), pages 493-506.
    9. Liu, Zhi-Feng & Li, Ling-Ling & Liu, Yu-Wei & Liu, Jia-Qi & Li, Heng-Yi & Shen, Qiang, 2021. "Dynamic economic emission dispatch considering renewable energy generation: A novel multi-objective optimization approach," Energy, Elsevier, vol. 235(C).
    10. Wei, Wei & Liu, Feng & Wang, Jianhui & Chen, Laijun & Mei, Shengwei & Yuan, Tiejiang, 2016. "Robust environmental-economic dispatch incorporating wind power generation and carbon capture plants," Applied Energy, Elsevier, vol. 183(C), pages 674-684.
    11. Kheshti, Mostafa & Ding, Lei & Ma, Shicong & Zhao, Bing, 2018. "Double weighted particle swarm optimization to non-convex wind penetrated emission/economic dispatch and multiple fuel option systems," Renewable Energy, Elsevier, vol. 125(C), pages 1021-1037.
    12. Saqib Akram & Muhammad Salman Fakhar & Syed Abdul Rahman Kashif & Ghulam Abbas & Nasim Ullah & Alsharef Mohammad & Mohamed Emad Farrag, 2022. "Introducing Adaptive Machine Learning Technique for Solving Short-Term Hydrothermal Scheduling with Prohibited Discharge Zones," Sustainability, MDPI, vol. 14(18), pages 1-18, September.
    13. Cong Dong & Xiucheng Dong & Joel Gehman & Lianne Lefsrud, 2017. "Using BP Neural Networks to Prioritize Risk Management Approaches for China’s Unconventional Shale Gas Industry," Sustainability, MDPI, vol. 9(6), pages 1-18, June.
    14. Ávalos-Ruiz, L.F. & Gómez-Aguilar, J.F. & Atangana, A. & Owolabi, Kolade M., 2019. "On the dynamics of fractional maps with power-law, exponential decay and Mittag–Leffler memory," Chaos, Solitons & Fractals, Elsevier, vol. 127(C), pages 364-388.
    15. Xiuyun Wang & Junyu Tian & Rutian Wang & Jiakai Xu & Shaoxin Chen & Jian Wang & Yang Cui, 2018. "Multi-Objective Economic Dispatch of Cogeneration Unit with Heat Storage Based on Fuzzy Chance Constraint," Energies, MDPI, vol. 12(1), pages 1-18, December.
    16. Tingli Cheng & Minyou Chen & Yingxiang Wang & Bo Li & Muhammad Arshad Shehzad Hassan & Tao Chen & Ruilin Xu, 2018. "Adaptive Robust Method for Dynamic Economic Emission Dispatch Incorporating Renewable Energy and Energy Storage," Complexity, Hindawi, vol. 2018, pages 1-13, June.
    17. Peng, Yuexi & Liu, Jun & He, Shaobo & Sun, Kehui, 2023. "Discrete fracmemristor-based chaotic map by Grunwald–Letnikov difference and its circuit implementation," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    18. Tutueva, Aleksandra V. & Karimov, Artur I. & Moysis, Lazaros & Volos, Christos & Butusov, Denis N., 2020. "Construction of one-way hash functions with increased key space using adaptive chaotic maps," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    19. Long Cai & Jie Gu & Jinghuan Ma & Zhijian Jin, 2019. "Probabilistic Wind Power Forecasting Approach via Instance-Based Transfer Learning Embedded Gradient Boosting Decision Trees," Energies, MDPI, vol. 12(1), pages 1-19, January.
    20. Dubey, Hari Mohan & Pandit, Manjaree & Panigrahi, B.K., 2015. "Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch," Renewable Energy, Elsevier, vol. 83(C), pages 188-202.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:4014-:d:588012. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.